The predictive value of [18F]FDG PET/CT radiomics combined with clinical features for EGFR mutation status in different clinical staging of lung adenocarcinoma
Abstract Background This study aims to construct radiomics models based on [18F]FDG PET/CT using multiple machine learning methods to predict the EGFR mutation status of lung adenocarcinoma and evaluate whether incorporating clinical parameters can improve the performance of radiomics models. Method...
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| Format: | Article |
| Language: | English |
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SpringerOpen
2023-04-01
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| Series: | EJNMMI Research |
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| Online Access: | https://doi.org/10.1186/s13550-023-00977-4 |
| _version_ | 1797849870217773056 |
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| author | Jianxiong Gao Rong Niu Yunmei Shi Xiaoliang Shao Zhenxing Jiang Xinyu Ge Yuetao Wang Xiaonan Shao |
| author_facet | Jianxiong Gao Rong Niu Yunmei Shi Xiaoliang Shao Zhenxing Jiang Xinyu Ge Yuetao Wang Xiaonan Shao |
| author_sort | Jianxiong Gao |
| collection | DOAJ |
| description | Abstract Background This study aims to construct radiomics models based on [18F]FDG PET/CT using multiple machine learning methods to predict the EGFR mutation status of lung adenocarcinoma and evaluate whether incorporating clinical parameters can improve the performance of radiomics models. Methods A total of 515 patients were retrospectively collected and divided into a training set (n = 404) and an independent testing set (n = 111) according to their examination time. After semi-automatic segmentation of PET/CT images, the radiomics features were extracted, and the best feature sets of CT, PET, and PET/CT modalities were screened out. Nine radiomics models were constructed using logistic regression (LR), random forest (RF), and support vector machine (SVM) methods. According to the performance in the testing set, the best model of the three modalities was kept, and its radiomics score (Rad-score) was calculated. Furthermore, combined with the valuable clinical parameters (gender, smoking history, nodule type, CEA, SCC-Ag), a joint radiomics model was built. Results Compared with LR and SVM, the RF Rad-score showed the best performance among the three radiomics models of CT, PET, and PET/CT (training and testing sets AUC: 0.688, 0.666, and 0.698 vs. 0.726, 0.678, and 0.704). Among the three joint models, the PET/CT joint model performed the best (training and testing sets AUC: 0.760 vs. 0.730). The further stratified analysis found that CT_RF had the best prediction effect for stage I–II lesions (training set and testing set AUC: 0.791 vs. 0.797), while PET/CT joint model had the best prediction effect for stage III–IV lesions (training and testing sets AUC: 0.722 vs. 0.723). Conclusions Combining with clinical parameters can improve the predictive performance of PET/CT radiomics model, especially for patients with advanced lung adenocarcinoma. |
| first_indexed | 2024-04-09T18:52:13Z |
| format | Article |
| id | doaj.art-ee5fd8fcadd34e44bb00b448a673af17 |
| institution | Directory Open Access Journal |
| issn | 2191-219X |
| language | English |
| last_indexed | 2024-04-09T18:52:13Z |
| publishDate | 2023-04-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | EJNMMI Research |
| spelling | doaj.art-ee5fd8fcadd34e44bb00b448a673af172023-04-09T11:26:44ZengSpringerOpenEJNMMI Research2191-219X2023-04-0113111310.1186/s13550-023-00977-4The predictive value of [18F]FDG PET/CT radiomics combined with clinical features for EGFR mutation status in different clinical staging of lung adenocarcinomaJianxiong Gao0Rong Niu1Yunmei Shi2Xiaoliang Shao3Zhenxing Jiang4Xinyu Ge5Yuetao Wang6Xiaonan Shao7Department of Nuclear Medicine, The Third Affiliated Hospital of Soochow UniversityDepartment of Nuclear Medicine, The Third Affiliated Hospital of Soochow UniversityDepartment of Nuclear Medicine, The Third Affiliated Hospital of Soochow UniversityDepartment of Nuclear Medicine, The Third Affiliated Hospital of Soochow UniversityDepartment of Radiology, The Third Affiliated Hospital of Soochow UniversityDepartment of Nuclear Medicine, The Third Affiliated Hospital of Soochow UniversityDepartment of Nuclear Medicine, The Third Affiliated Hospital of Soochow UniversityDepartment of Nuclear Medicine, The Third Affiliated Hospital of Soochow UniversityAbstract Background This study aims to construct radiomics models based on [18F]FDG PET/CT using multiple machine learning methods to predict the EGFR mutation status of lung adenocarcinoma and evaluate whether incorporating clinical parameters can improve the performance of radiomics models. Methods A total of 515 patients were retrospectively collected and divided into a training set (n = 404) and an independent testing set (n = 111) according to their examination time. After semi-automatic segmentation of PET/CT images, the radiomics features were extracted, and the best feature sets of CT, PET, and PET/CT modalities were screened out. Nine radiomics models were constructed using logistic regression (LR), random forest (RF), and support vector machine (SVM) methods. According to the performance in the testing set, the best model of the three modalities was kept, and its radiomics score (Rad-score) was calculated. Furthermore, combined with the valuable clinical parameters (gender, smoking history, nodule type, CEA, SCC-Ag), a joint radiomics model was built. Results Compared with LR and SVM, the RF Rad-score showed the best performance among the three radiomics models of CT, PET, and PET/CT (training and testing sets AUC: 0.688, 0.666, and 0.698 vs. 0.726, 0.678, and 0.704). Among the three joint models, the PET/CT joint model performed the best (training and testing sets AUC: 0.760 vs. 0.730). The further stratified analysis found that CT_RF had the best prediction effect for stage I–II lesions (training set and testing set AUC: 0.791 vs. 0.797), while PET/CT joint model had the best prediction effect for stage III–IV lesions (training and testing sets AUC: 0.722 vs. 0.723). Conclusions Combining with clinical parameters can improve the predictive performance of PET/CT radiomics model, especially for patients with advanced lung adenocarcinoma.https://doi.org/10.1186/s13550-023-00977-4lung adenocarcinoma[18F]FDG PET/CTRadiomicsEpidermal growth factor receptorPrediction |
| spellingShingle | Jianxiong Gao Rong Niu Yunmei Shi Xiaoliang Shao Zhenxing Jiang Xinyu Ge Yuetao Wang Xiaonan Shao The predictive value of [18F]FDG PET/CT radiomics combined with clinical features for EGFR mutation status in different clinical staging of lung adenocarcinoma EJNMMI Research lung adenocarcinoma [18F]FDG PET/CT Radiomics Epidermal growth factor receptor Prediction |
| title | The predictive value of [18F]FDG PET/CT radiomics combined with clinical features for EGFR mutation status in different clinical staging of lung adenocarcinoma |
| title_full | The predictive value of [18F]FDG PET/CT radiomics combined with clinical features for EGFR mutation status in different clinical staging of lung adenocarcinoma |
| title_fullStr | The predictive value of [18F]FDG PET/CT radiomics combined with clinical features for EGFR mutation status in different clinical staging of lung adenocarcinoma |
| title_full_unstemmed | The predictive value of [18F]FDG PET/CT radiomics combined with clinical features for EGFR mutation status in different clinical staging of lung adenocarcinoma |
| title_short | The predictive value of [18F]FDG PET/CT radiomics combined with clinical features for EGFR mutation status in different clinical staging of lung adenocarcinoma |
| title_sort | predictive value of 18f fdg pet ct radiomics combined with clinical features for egfr mutation status in different clinical staging of lung adenocarcinoma |
| topic | lung adenocarcinoma [18F]FDG PET/CT Radiomics Epidermal growth factor receptor Prediction |
| url | https://doi.org/10.1186/s13550-023-00977-4 |
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